本文已被:浏览 1415次 下载 2316次
Received:September 01, 2016 Revised:October 10, 2016
Received:September 01, 2016 Revised:October 10, 2016
中文摘要: 互联网上出现越来越多的云服务,面对种类繁多的云服务,如何准确地在众多云服务中把符合用户需求并且性能好价格低的服务推荐给用户成为云服务推荐的研究热点.现有的服务推荐方法往往只是根据当前云服务的历史性能记录为用户进行推荐,并没有充分考虑云服务的性能趋势.针对上述问题,本文提出了一种基于性能预测的服务推荐模型,该模型利用共轭梯度改进人工神经网络对云服务的性能进行预测,使用层次分析法对性能,价格等因素进行综合比较计算,最终为用户推荐最为合适的云服务.实验结果表明,使用改进神经网络对服务性能进行预测能够获得较高的准确度,层次分析法可以综合考虑服务的性能与价格,为用户推荐最为合适的云服务.
Abstract:A growing number of cloud services have emerged on the Internet. In the face of a wide variety of cloud services, how to recommend high quality and low price service meeting the users' requirements to the user accurately has become a focus in cloud service recommendation field. Currently, many services recommendation methods are often just based on the current service status without taking into account the performance trend of cloud service. For this reason, this paper proposes a services recommendation model based on performance prediction. The model uses improved artificial neural network based on conjugate gradient to predict the performance of cloud services. Factors such as performance and prices can be compared and calculated by using AHP (Analytic Hierarchy Process), and then the most suitable cloud service would be recommended to the users. The experimental results show that the prediction accuracy would be higher by using improved neural network predicting service performance method, and AHP can recommend the most suitable service to the user according to comprehensively considering the performance and price of services.
文章编号: 中图分类号: 文献标志码:
基金项目:
引用文本:
汪佳祯,迟焕醒,王木涵,史双田.基于性能感知预测的云服务推荐模型.计算机系统应用,2017,26(5):210-214
WANG Jia-Zhen,CHI Huan-Xing,WANG Mu-Han,SHI Shuang-Tian.Cloud Service Recommendation Model Based on Performance Prediction.COMPUTER SYSTEMS APPLICATIONS,2017,26(5):210-214
汪佳祯,迟焕醒,王木涵,史双田.基于性能感知预测的云服务推荐模型.计算机系统应用,2017,26(5):210-214
WANG Jia-Zhen,CHI Huan-Xing,WANG Mu-Han,SHI Shuang-Tian.Cloud Service Recommendation Model Based on Performance Prediction.COMPUTER SYSTEMS APPLICATIONS,2017,26(5):210-214